The 3-Month Framework: A Pragmatic Approach to Unscalable Solutions

In the world of startups, one piece of advice resonates widely: “Do things that don’t scale,” a principle popularized by Paul Graham. However, few discuss how to effectively integrate this philosophy into software development. After eight months of building my AI podcast platform, I’ve arrived at a practical strategy: any unscalable solution is given a three-month trial period. If it proves valuable, it gets a robust redesign; if not, it’s time to let it go.

As engineers, we are often conditioned to think in terms of scalable architectures right from the start—microservices, robust databases, and complex systems designed to handle millions of users. This mindset, while beneficial for established companies, can be detrimental in the startup environment, where scalable solutions might merely serve as a costly form of procrastination. Instead of solving real-time issues for an unknown volume of users, I advocate for delivering simple, straightforward, and sometimes imperfect code that directly addresses current needs.

The Power of My 3-Month Rule

Here are several unconventional infrastructure decisions I’ve made that have proven to be effective:

1. Consolidation on a Single Virtual Machine

All essential services—database, web server, background jobs, and caching—operate on a single $40/month virtual machine without redundancy, coupled with manual backups. This setup initially may seem reckless, yet it’s revealed invaluable insights about my actual resource demands. Within two months of operation, I learned that my AI-driven platform peaks at just 4GB of RAM. Had I pursued a complex Kubernetes architecture, I’d be managing idle containers instead of real-time operations. Moreover, every crash (which has happened twice) has uncovered previously unseen vulnerabilities in my system.

2. Hardcoded Configurations

Throughout my codebase, constants define critical configuration values rather than relying on external files or environment variables. This approach simplifies searchability; I can quickly identify any configuration through a simple command. It turns out that I’ve only modified these parameters three times in three months. Each alteration took a mere 15 minutes to redeploy, a stark contrast to the hours it would have required to set up a separate configuration service.

3. Utilizing SQLite in Production

Contrary to conventional wisdom, my multi-user application runs on SQLite—a decision that has not hindered performance. The entire database is merely 47MB and manages 50 concurrent users seamlessly. This has taught me